Kroese / Taimre / Botev Handbook of Monte Carlo Methods

1. Auflage 2011
ISBN: 978-1-118-01494-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 772 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

A comprehensive overview of Monte Carlo simulation that exploresthe latest topics, techniques, and real-world applications
More and more of today's numerical problems found inengineering and finance are solved through Monte Carlo methods. Theheightened popularity of these methods and their continuingdevelopment makes it important for researchers to have acomprehensive understanding of the Monte Carlo approach.Handbook of Monte Carlo Methods provides the theory,algorithms, and applications that helps provide a thoroughunderstanding of the emerging dynamics of this rapidly-growingfield.
The authors begin with a discussion of fundamentals such as howto generate random numbers on a computer. Subsequent chaptersdiscuss key Monte Carlo topics and methods, including:
* Random variable and stochastic process generation
* Markov chain Monte Carlo, featuring key algorithms such as theMetropolis-Hastings method, the Gibbs sampler, and hit-and-run
* Discrete-event simulation
* Techniques for the statistical analysis of simulation dataincluding the delta method, steady-state estimation, and kerneldensity estimation
* Variance reduction, including importance sampling, latinhypercube sampling, and conditional Monte Carlo
* Estimation of derivatives and sensitivity analysis
* Advanced topics including cross-entropy, rare events, kerneldensity estimation, quasi Monte Carlo, particle systems, andrandomized optimization
The presented theoretical concepts are illustrated with workedexamples that use MATLAB¯®, a related Web sitehouses the MATLAB¯® code, allowing readers to workhands-on with the material and also features the author's ownlecture notes on Monte Carlo methods. Detailed appendices providebackground material on probability theory, stochastic processes,and mathematical statistics as well as the key optimizationconcepts and techniques that are relevant to Monte Carlosimulation.
Handbook of Monte Carlo Methods is an excellent referencefor applied statisticians and practitioners working in the fieldsof engineering and finance who use or would like to learn how touse Monte Carlo in their research. It is also a suitable supplementfor courses on Monte Carlo methods and computational statistics atthe upper-undergraduate and graduate levels.

Autoren/Hrsg.

Kroese, Dirk P.

Taimre, Thomas

Botev, Zdravko I.

Über Autor(innen)

Dirk P. Kroese, PhD, is Australian Professorial Fellow inStatistics at The University of Queensland (Australia). Dr. Kroesehas more than seventy publications in such areas as stochasticmodeling, randomized algorithms, computational statistics, andreliability. He is a pioneer of the cross-entropy method and thecoauthor of Simulation and the Monte Carlo Method, Second Edition(Wiley).
Thomas Taimre, PhD, is a Postdoctoral Research Fellow atThe University of Queensland. He currently focuses his research onMonte Carlo methods and simulation, from the theoreticalfoundations to performing computer implementations.
Zdravko I. Botev, PhD, is a Postdoctoral ResearchFellow at the University of Montreal (Canada). His researchinterests include the splitting method for rare-event simulationand kernel density estimation. He is the author of one of the mostwidely used free MATLAB® statistical software programs fornonparametric kernel density estimation.